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On This Book: Clustering, Multisets, Rough Sets and Fuzzy Sets Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Robust Clustering Algorithms Employing Fuzzy-Possibilistic Product Partition

Author : László Szilágyi

Published in: Fuzzy Sets, Rough Sets, Multisets and Clustering

Publisher: Springer International Publishing

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Abstract

One of the main challenges in the field of clustering is creating algorithms that are both accurate and robust. The fuzzy-possibilistic product partition c-means clustering algorithm was introduced with the main goal of producing accurate partitions in the presence of outlier data. This chapter presents several clustering algorithms based on the fuzzy-possibilistic product partition, specialized for the detection of clusters having various shapes including spherical and ellipsoidal shells. The advantages of applying the fuzzy-possibilistic product partition are presented in comparison with previous c-means clustering models. Besides being more robust and accurate than previous probabilistic-possibilistic mixture partitions, the product partition is easier to handle due to its reduced number of parameters.

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previous chapter On Some Clustering Algorithms Based on Tolerance
next chapter Consensus-Based Agglomerative Hierarchical Clustering
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Metadata
Title
Robust Clustering Algorithms Employing Fuzzy-Possibilistic Product Partition
Author
László Szilágyi
Copyright Year
2017
Book
Fuzzy Sets, Rough Sets, Multisets and Clustering

Print ISBN: 978-3-319-47556-1

Electronic ISBN: 978-3-319-47557-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-47557-8_7

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